Cumulative Plot Sales

Column

Plots Sold Across the World

Row

Plot Locations

Plots Sold in the US

World Plots

Column

Cumulative Plot Sales

country sold
United States 3969
United Kingdom 461
France 273
Italy 198
Australia 187
Switzerland 185
Japan 177
United Arab Emirates 155
Canada 129
Spain 124
Singapore 105
China 101
Germany 96
Netherlands 71
Hong Kong 63
Egypt 62
Mexico 59
India 53
Austria 51
Portugal 49
Russia 47
Brazil 44
Monaco 39
Turkey 39
Peru 35
Greece 34
Poland 33
Thailand 32
Uzbekistan 31
Vatican City 31
Cambodia 28
Israel 28
South Korea 26
Belgium 20
Saudi Arabia 19
Indonesia 18
New Zealand 15
Panama 15
Qatar 14
South Africa 12
Ukraine 12
coordinates 11
Czech Republic 10
Colombia 8
Finland 8
NA 7
Iraq 6
Ireland 6
Jamaica 5
Jordan 5
Zimbabwe 5
Argentina 4
Denmark 4
Georgia 4
Malaysia 4
Malta 4
Philippines 4
Romania 4
Venezuela 4
Bahamas 3
Cuba 3
Iceland 3
Kazakhstan 3
Saint Barthelemy 3
Saint Lucia 3
Taiwan 3
Armenia 2
Azerbaijan 2
Croatia 2
Dominican Republic 2
French Polynesia 2
Ghana 2
Guatemala 2
Nepal 2
Ontario 2
Senegal 2
Bolivia 1
Bulgaria 1
Chad 1
Chile 1
Costa Rica 1
Cyprus 1
Estonia 1
Ethiopia 1
Hungary 1
Jerusalem District 1
Luxembourg 1
Mongolia 1
Montenegro 1
North Korea 1
Norway 1
Palestinian Territories 1
Serbia 1
Sri Lanka 1
Sweden 1
US Virgin Islands 1
Vietnam 1

Column

Day

Week

Month

Year

Total

US Plots

Column

Cumulative US Plot Sales

state sold
California 745
Texas 738
New York 684
Florida 416
Nevada 313
Illinois 109
District of Columbia 101
Massachusetts 72
Tennessee 70
Pennsylvania 58
Louisiana 56
Georgia 48
Washington 44
Minnesota 36
North Carolina 36
Hawaii 35
Indiana 34
Kentucky 34
Ohio 33
New Jersey 31
Missouri 30
Michigan 29
Arizona 27
Colorado 24
Maryland 23
Wisconsin 18
Virginia 17
Alabama 16
Oregon 15
Wyoming 14
Oklahoma 13
Arkansas 12
Utah 11
Connecticut 4
South Carolina 4
Maine 3
New Mexico 3
South Dakota 3
Mississippi 2
Delaware 1
Iowa 1
Kansas 1
Nebraska 1
New Hampshire 1
New York L2GX5 1
North Dakota 1
Ontario 1

Column

Day

Week

Month

Year

Total

---
title: "SuperWorld Plot Sales"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    source_code: embed
    theme: yeti
---

Cumulative Plot Sales
=====================================

Inputs {.sidebar}
-------------------------------------

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
library(leaflet)
library(leaflet.extras)
library(sf)
library(tidyverse)
library(rnaturalearth)
library(rnaturalearthdata)
library(plotly)
library(usmap)
library(lubridate)

plots_sold = read_csv("C:/Users/rebec/SuperWorld_Plot_Recommendation/data/plots_sold.csv")[-1]
plots_sold$code = toupper(plots_sold$code)

us_plots = plots_sold[which(plots_sold$code == "US"),]
us_address = us_plots$address

state = c()
for (i in 1:length(us_address)){
  add = tail(unlist(str_split(us_address[i], pattern = ", ")), 2)[1]
  add = gsub(' [[:digit:]]+', '', add)
  state = c(state, add)
}

us_plots = cbind(us_plots, state) 

state_data = data.frame(state) %>%
  group_by(state) %>%
  summarise(sold = n())

```

*Total Plot Sales:*

```{r}
nrow(plots_sold)
```


*Top 10 Countries:* ```{r} plots_sold %>% group_by(country) %>% summarise(`plots sold` = n()) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ```
*Top 10 US States:* ```{r} state_data %>% summarise(state, `plots sold` = sold) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ``` Column {data-width=800} ------------------------------------- ### Plots Sold Across the World ```{r warning=FALSE, message=FALSE} world = ne_countries(scale = "medium", returnclass = "sf") df = st_sf(merge(plots_sold, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) df_plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradient(trans = "log") + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) # df2 = df %>% # group_by(country, code) %>% # summarise(sold = n()) %>% # mutate(sold = ifelse(is.na(country), 0, sold)) # plot(df2["sold"], logz = TRUE, main = NULL, key.pos = 4) ggplotly(df_plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` Row ------------------------------------- ### Plot Locations ```{r} leaflet(plots_sold) %>% addTiles() %>% addCircles(lng = ~lon, lat = ~lat) %>% setView(lat = 37.0902, lng = -95.7129, zoom = 4) ``` ### Plots Sold in the US ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_continuous(name = "Plots Sold") ggplotly(us) ``` World Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative Plot Sales ```{r} plots_sold %>% group_by(country) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} plots_today = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) df_today = st_sf(merge(plots_today, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_today %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE)) ``` ### Week ```{r} plots_week = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) df_week = st_sf(merge(plots_week, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_week %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Month ```{r} plots_month = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) df_month = st_sf(merge(plots_month, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_month %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Year ```{r} plots_year = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) df_year = st_sf(merge(plots_year, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_year %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(5, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Total ```{r} plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` US Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative US Plot Sales ```{r} us_plots %>% group_by(state) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} us_today = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) %>% group_by(state) %>% summarise(sold = n()) us_today = plot_usmap(data = us_today, values = "sold", regions = "states") + theme(legend.position = "right") + # scale_fill_continuous(name = "Plots Sold") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_today) ``` ### Week ```{r} us_week = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) %>% group_by(state) %>% summarise(sold = n()) us_week = plot_usmap(data = us_week, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_week) ``` ### Month ```{r} us_month = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) %>% group_by(state) %>% summarise(sold = n()) us_month = plot_usmap(data = us_month, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_month) ``` ### Year ```{r} us_year = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) %>% group_by(state) %>% summarise(sold = n()) us_year = plot_usmap(data = us_year, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_year) ``` ### Total ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us) ```